4 citations · 8 across the 5 of their papers we have counts for
5 papers
Global Group Fairness in Federated Learning via Function Tracking
Yves Rychener, Daniel Kuhn, Yifan Hu
We investigate group fairness regularizers in federated learning, aiming to train a globally fair model in a distributed setting. Ensuring global fairness in distributed training p…
Wasserstein Distributionally Robust Optimization with Heterogeneous Data Sources
Yves Rychener, Adrian Esteban-Perez, Juan M. Morales +1
We study decision problems under uncertainty, where the decision-maker has access to data sources that carry {\em biased} information about the underlying risk factors. The bia…
A Geometric Unification of Distributionally Robust Covariance Estimators: Shrinking the Spectrum by Inflating the Ambiguity Set
Man-Chung Yue, Yves Rychener, Daniel Kuhn +1
The state-of-the-art methods for estimating high-dimensional covariance matrices all shrink the eigenvalues of the sample covariance matrix towards a data-insensitive shrinkage tar…
End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
Yves Rychener, Daniel Kuhn, Tobias Sutter
We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation…
Metrizing Fairness
Yves Rychener, Bahar Taskesen, Daniel Kuhn
We study supervised learning problems that have significant effects on individuals from two demographic groups, and we seek predictors that are fair with respect to a group fairnes…